Attribute-Centered Loss for Soft-Biometrics Guided Face Sketch-Photo Recognition

TL;DR

This paper introduces an attribute-centered loss for training a deep neural network to improve face sketch-photo recognition by incorporating facial attribute information, significantly enhancing accuracy over existing methods.

Contribution

It proposes a novel attribute-centered loss function that learns distinct centers for different attribute combinations, improving face sketch-photo matching accuracy.

Findings

Significantly outperforms state-of-the-art methods on benchmark datasets.

Effectively incorporates facial attributes into the recognition process.

Enhances spatial topology preservation in face matching.

Abstract

Face sketches are able to capture the spatial topology of a face while lacking some facial attributes such as race, skin, or hair color. Existing sketch-photo recognition approaches have mostly ignored the importance of facial attributes. In this paper, we propose a new loss function, called attribute-centered loss, to train a Deep Coupled Convolutional Neural Network (DCCNN) for the facial attribute guided sketch to photo matching. Specifically, an attribute-centered loss is proposed which learns several distinct centers, in a shared embedding space, for photos and sketches with different combinations of attributes. The DCCNN simultaneously is trained to map photos and pairs of testified attributes and corresponding forensic sketches around their associated centers, while preserving the spatial topology information. Importantly, the centers learn to keep a relative distance from each other, related to their number of contradictory attributes. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D Semi-forensic) databases. The proposed method significantly outperforms the state-of-the-art.